Overview

Dataset statistics

Number of variables12
Number of observations46633944
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.2 GiB
Average record size in memory96.0 B

Variable types

DateTime1
Categorical2
Numeric9

Alerts

lat,lon has a high cardinality: 2651 distinct valuesHigh cardinality
components.co is highly overall correlated with components.no2 and 3 other fieldsHigh correlation
components.no2 is highly overall correlated with components.co and 5 other fieldsHigh correlation
components.o3 is highly overall correlated with components.no2High correlation
components.so2 is highly overall correlated with components.no2High correlation
components.pm2_5 is highly overall correlated with components.co and 2 other fieldsHigh correlation
components.pm10 is highly overall correlated with components.co and 2 other fieldsHigh correlation
components.nh3 is highly overall correlated with components.co and 1 other fieldsHigh correlation
components.no has 15286768 (32.8%) zerosZeros
components.o3 has 1257649 (2.7%) zerosZeros
components.nh3 has 1699413 (3.6%) zerosZeros

Reproduction

Analysis started2023-01-11 10:14:04.939884
Analysis finished2023-01-11 10:46:45.250778
Duration32 minutes and 40.31 seconds
Software versionpandas-profiling vv3.6.2
Download configurationconfig.json

Variables

dt
Date

Distinct17328
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size355.8 MiB
Minimum2021-01-01 00:00:00
Maximum2022-12-31 23:00:00
2023-01-11T11:46:45.368780image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T11:46:45.454779image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

main.aqi
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size355.8 MiB
1
23120252 
2
15958819 
3
3547273 
4
2637846 
5
 
1369754

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters46633944
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row2

Common Values

ValueCountFrequency (%)
1 23120252
49.6%
2 15958819
34.2%
3 3547273
 
7.6%
4 2637846
 
5.7%
5 1369754
 
2.9%

Length

2023-01-11T11:46:45.536778image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-11T11:46:45.619300image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1 23120252
49.6%
2 15958819
34.2%
3 3547273
 
7.6%
4 2637846
 
5.7%
5 1369754
 
2.9%

Most occurring characters

ValueCountFrequency (%)
1 23120252
49.6%
2 15958819
34.2%
3 3547273
 
7.6%
4 2637846
 
5.7%
5 1369754
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 46633944
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 23120252
49.6%
2 15958819
34.2%
3 3547273
 
7.6%
4 2637846
 
5.7%
5 1369754
 
2.9%

Most occurring scripts

ValueCountFrequency (%)
Common 46633944
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 23120252
49.6%
2 15958819
34.2%
3 3547273
 
7.6%
4 2637846
 
5.7%
5 1369754
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 46633944
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 23120252
49.6%
2 15958819
34.2%
3 3547273
 
7.6%
4 2637846
 
5.7%
5 1369754
 
2.9%

components.co
Real number (ℝ)

Distinct956
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean322.23665
Minimum77.61
Maximum7583.62
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size355.8 MiB
2023-01-11T11:46:45.692300image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum77.61
5-th percentile175.24
Q1220.3
median263.69
Q3333.79
95-th percentile674.25
Maximum7583.62
Range7506.01
Interquartile range (IQR)113.49

Descriptive statistics

Standard deviation221.0768
Coefficient of variation (CV)0.6860697
Kurtosis49.495009
Mean322.23665
Median Absolute Deviation (MAD)51.74
Skewness5.4298716
Sum1.5027166 × 1010
Variance48874.951
MonotonicityNot monotonic
2023-01-11T11:46:45.777296image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
230.31 950270
 
2.0%
233.65 948983
 
2.0%
226.97 946941
 
2.0%
223.64 942458
 
2.0%
236.99 937625
 
2.0%
240.33 925674
 
2.0%
220.3 924791
 
2.0%
243.66 906438
 
1.9%
216.96 900053
 
1.9%
247 888640
 
1.9%
Other values (946) 37362071
80.1%
ValueCountFrequency (%)
77.61 1
 
< 0.1%
78.44 2
 
< 0.1%
80.94 3
< 0.1%
82.61 2
 
< 0.1%
83.45 1
 
< 0.1%
84.28 5
< 0.1%
85.12 1
 
< 0.1%
85.95 4
< 0.1%
86.78 3
< 0.1%
87.62 4
< 0.1%
ValueCountFrequency (%)
7583.62 1
 
< 0.1%
7370 1
 
< 0.1%
6622.31 1
 
< 0.1%
6462.1 16
< 0.1%
6355.29 34
< 0.1%
6248.47 1
 
< 0.1%
6195.07 16
< 0.1%
6141.66 31
< 0.1%
6088.26 2
 
< 0.1%
6034.85 22
< 0.1%

components.no
Real number (ℝ)

Distinct1461
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.8372645
Minimum0
Maximum865.46
Zeros15286768
Zeros (%)32.8%
Negative0
Negative (%)0.0%
Memory size355.8 MiB
2023-01-11T11:46:45.868303image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.13
Q30.98
95-th percentile24.14
Maximum865.46
Range865.46
Interquartile range (IQR)0.98

Descriptive statistics

Standard deviation20.905834
Coefficient of variation (CV)4.3218297
Kurtosis127.84661
Mean4.8372645
Median Absolute Deviation (MAD)0.13
Skewness9.3230792
Sum2.2558072 × 108
Variance437.05388
MonotonicityNot monotonic
2023-01-11T11:46:45.952301image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 15286768
32.8%
0.01 1992832
 
4.3%
0.02 1005038
 
2.2%
0.03 737916
 
1.6%
0.04 596139
 
1.3%
0.05 524989
 
1.1%
0.06 499046
 
1.1%
0.07 466281
 
1.0%
0.08 448063
 
1.0%
0.09 429436
 
0.9%
Other values (1451) 24647436
52.9%
ValueCountFrequency (%)
0 15286768
32.8%
0.01 1992832
 
4.3%
0.02 1005038
 
2.2%
0.03 737916
 
1.6%
0.04 596139
 
1.3%
0.05 524989
 
1.1%
0.06 499046
 
1.1%
0.07 466281
 
1.0%
0.08 448063
 
1.0%
0.09 429436
 
0.9%
ValueCountFrequency (%)
865.46 16
< 0.1%
851.15 16
< 0.1%
808.24 16
< 0.1%
772.48 16
< 0.1%
765.32 16
< 0.1%
729.56 16
< 0.1%
715.26 15
< 0.1%
708.1 16
< 0.1%
700.95 28
< 0.1%
693.8 22
< 0.1%

components.no2
Real number (ℝ)

Distinct1306
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.295924
Minimum0.02
Maximum460.62
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size355.8 MiB
2023-01-11T11:46:46.037774image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.02
5-th percentile1.01
Q13.34
median7.71
Q319.19
95-th percentile63.06
Maximum460.62
Range460.6
Interquartile range (IQR)15.85

Descriptive statistics

Standard deviation22.486814
Coefficient of variation (CV)1.3799042
Kurtosis15.760183
Mean16.295924
Median Absolute Deviation (MAD)5.49
Skewness3.1586302
Sum7.5994322 × 108
Variance505.65681
MonotonicityNot monotonic
2023-01-11T11:46:46.119951image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.57 221117
 
0.5%
5.74 215291
 
0.5%
5.83 210790
 
0.5%
5.91 209397
 
0.4%
6 208694
 
0.4%
6.08 205486
 
0.4%
11.14 205277
 
0.4%
6.17 201904
 
0.4%
11.31 201442
 
0.4%
11.48 198665
 
0.4%
Other values (1296) 44555881
95.5%
ValueCountFrequency (%)
0.02 56
 
< 0.1%
0.03 186
 
< 0.1%
0.04 449
 
< 0.1%
0.05 770
 
< 0.1%
0.06 1100
 
< 0.1%
0.07 1417
< 0.1%
0.08 1839
< 0.1%
0.09 2269
< 0.1%
0.1 3113
< 0.1%
0.11 3296
< 0.1%
ValueCountFrequency (%)
460.62 16
 
< 0.1%
449.66 62
< 0.1%
433.21 69
< 0.1%
416.76 83
< 0.1%
411.27 48
< 0.1%
405.79 62
< 0.1%
400.3 54
< 0.1%
394.82 72
< 0.1%
389.34 24
 
< 0.1%
378.37 74
< 0.1%

components.o3
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1360
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean60.479011
Minimum0
Maximum823.98
Zeros1257649
Zeros (%)2.7%
Negative0
Negative (%)0.0%
Memory size355.8 MiB
2023-01-11T11:46:46.204677image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.4
Q132.9
median58.65
Q382.25
95-th percentile125.89
Maximum823.98
Range823.98
Interquartile range (IQR)49.35

Descriptive statistics

Standard deviation40.484896
Coefficient of variation (CV)0.66940407
Kurtosis4.7504424
Mean60.479011
Median Absolute Deviation (MAD)24.68
Skewness1.2158001
Sum2.8203748 × 109
Variance1639.0268
MonotonicityNot monotonic
2023-01-11T11:46:46.283417image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1257649
 
2.7%
92.98 428300
 
0.9%
94.41 404396
 
0.9%
95.84 382415
 
0.8%
65.8 376007
 
0.8%
65.09 375258
 
0.8%
66.52 375136
 
0.8%
60.8 374798
 
0.8%
62.23 374760
 
0.8%
62.94 372934
 
0.8%
Other values (1350) 41912291
89.9%
ValueCountFrequency (%)
0 1257649
2.7%
0.01 192447
 
0.4%
0.02 102880
 
0.2%
0.03 73136
 
0.2%
0.04 58981
 
0.1%
0.05 47758
 
0.1%
0.06 43477
 
0.1%
0.07 36569
 
0.1%
0.08 32011
 
0.1%
0.09 30756
 
0.1%
ValueCountFrequency (%)
823.98 1
 
< 0.1%
732.42 1
 
< 0.1%
703.81 2
 
< 0.1%
663.76 3
 
< 0.1%
658.04 1
 
< 0.1%
652.31 15
< 0.1%
646.59 2
 
< 0.1%
640.87 19
< 0.1%
629.43 15
< 0.1%
629.42 3
 
< 0.1%

components.so2
Real number (ℝ)

Distinct913
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8760128
Minimum0
Maximum125.89
Zeros8794
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size355.8 MiB
2023-01-11T11:46:46.740829image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.19
Q10.73
median1.61
Q33.34
95-th percentile9.78
Maximum125.89
Range125.89
Interquartile range (IQR)2.61

Descriptive statistics

Standard deviation4.1221488
Coefficient of variation (CV)1.433286
Kurtosis39.742988
Mean2.8760128
Median Absolute Deviation (MAD)1.07
Skewness4.825446
Sum1.3411982 × 108
Variance16.992111
MonotonicityNot monotonic
2023-01-11T11:46:46.823716image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.95 274315
 
0.6%
0.51 264120
 
0.6%
1.94 262059
 
0.6%
0.54 261201
 
0.6%
0.57 259420
 
0.6%
1.97 257799
 
0.6%
0.6 257668
 
0.6%
2 254906
 
0.5%
0.63 253794
 
0.5%
0.66 250989
 
0.5%
Other values (903) 44037673
94.4%
ValueCountFrequency (%)
0 8794
 
< 0.1%
0.01 58587
0.1%
0.02 83323
0.2%
0.03 93445
0.2%
0.04 98837
0.2%
0.05 113276
0.2%
0.06 115594
0.2%
0.07 123759
0.3%
0.08 133442
0.3%
0.09 138037
0.3%
ValueCountFrequency (%)
125.89 2
< 0.1%
120.16 1
< 0.1%
119.21 1
< 0.1%
118.26 1
< 0.1%
117.3 2
< 0.1%
116.35 1
< 0.1%
114.44 1
< 0.1%
113.49 2
< 0.1%
112.53 1
< 0.1%
109.67 1
< 0.1%

components.pm2_5
Real number (ℝ)

Distinct21689
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.418874
Minimum0.5
Maximum3928.36
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size355.8 MiB
2023-01-11T11:46:46.907641image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.5
5-th percentile0.91
Q13
median5.79
Q311.02
95-th percentile34.36
Maximum3928.36
Range3927.86
Interquartile range (IQR)8.02

Descriptive statistics

Standard deviation17.340684
Coefficient of variation (CV)1.6643529
Kurtosis546.92787
Mean10.418874
Median Absolute Deviation (MAD)3.4
Skewness10.435513
Sum4.8587321 × 108
Variance300.69932
MonotonicityNot monotonic
2023-01-11T11:46:46.989193image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.5 1020728
 
2.2%
2.44 49677
 
0.1%
2.17 49582
 
0.1%
2.42 49503
 
0.1%
2.4 49184
 
0.1%
2.74 49148
 
0.1%
2.15 48761
 
0.1%
2.63 48742
 
0.1%
2.48 48736
 
0.1%
2.19 48701
 
0.1%
Other values (21679) 45171182
96.9%
ValueCountFrequency (%)
0.5 1020728
2.2%
0.51 28900
 
0.1%
0.52 29833
 
0.1%
0.53 29205
 
0.1%
0.54 29387
 
0.1%
0.55 29369
 
0.1%
0.56 31354
 
0.1%
0.57 30415
 
0.1%
0.58 29510
 
0.1%
0.59 29883
 
0.1%
ValueCountFrequency (%)
3928.36 1
< 0.1%
3842.74 1
< 0.1%
3422.77 1
< 0.1%
3284.55 1
< 0.1%
3167.91 1
< 0.1%
3078.2 1
< 0.1%
2911.62 1
< 0.1%
2878.22 1
< 0.1%
2778 1
< 0.1%
2651.44 1
< 0.1%

components.pm10
Real number (ℝ)

Distinct24591
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.664832
Minimum0.5
Maximum3928.62
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size355.8 MiB
2023-01-11T11:46:47.077139image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.5
5-th percentile1.32
Q14.49
median8.92
Q316.55
95-th percentile46.6
Maximum3928.62
Range3928.12
Interquartile range (IQR)12.06

Descriptive statistics

Standard deviation21.146175
Coefficient of variation (CV)1.441965
Kurtosis267.11637
Mean14.664832
Median Absolute Deviation (MAD)5.29
Skewness7.7102106
Sum6.8387895 × 108
Variance447.1607
MonotonicityNot monotonic
2023-01-11T11:46:47.159161image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.56 37465
 
0.1%
0.6 36677
 
0.1%
0.57 36074
 
0.1%
0.62 36032
 
0.1%
0.61 36010
 
0.1%
0.65 35994
 
0.1%
0.51 35942
 
0.1%
0.59 35935
 
0.1%
0.64 35624
 
0.1%
0.63 35607
 
0.1%
Other values (24581) 46272584
99.2%
ValueCountFrequency (%)
0.5 18154
< 0.1%
0.51 35942
0.1%
0.52 33480
0.1%
0.53 33550
0.1%
0.54 34522
0.1%
0.55 35458
0.1%
0.56 37465
0.1%
0.57 36074
0.1%
0.58 34767
0.1%
0.59 35935
0.1%
ValueCountFrequency (%)
3928.62 1
< 0.1%
3843.06 1
< 0.1%
3422.97 1
< 0.1%
3284.94 1
< 0.1%
3168.33 1
< 0.1%
3081.83 1
< 0.1%
2915.84 1
< 0.1%
2881.86 1
< 0.1%
2778.41 1
< 0.1%
2651.75 1
< 0.1%

components.nh3
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct580
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4414392
Minimum0
Maximum172.26
Zeros1699413
Zeros (%)3.6%
Negative0
Negative (%)0.0%
Memory size355.8 MiB
2023-01-11T11:46:47.247816image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.03
Q10.29
median0.7
Q31.58
95-th percentile5.26
Maximum172.26
Range172.26
Interquartile range (IQR)1.29

Descriptive statistics

Standard deviation2.4913579
Coefficient of variation (CV)1.7283822
Kurtosis138.02426
Mean1.4414392
Median Absolute Deviation (MAD)0.5
Skewness7.5952326
Sum67219996
Variance6.2068643
MonotonicityNot monotonic
2023-01-11T11:46:47.327576image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1699413
 
3.6%
0.27 438291
 
0.9%
0.28 431824
 
0.9%
0.55 414610
 
0.9%
0.14 410292
 
0.9%
0.25 408582
 
0.9%
0.34 405274
 
0.9%
0.59 396734
 
0.9%
0.36 392979
 
0.8%
0.2 386240
 
0.8%
Other values (570) 41249705
88.5%
ValueCountFrequency (%)
0 1699413
3.6%
0.01 217986
 
0.5%
0.02 222140
 
0.5%
0.03 241559
 
0.5%
0.04 264547
 
0.6%
0.05 300983
 
0.6%
0.06 304057
 
0.7%
0.07 320969
 
0.7%
0.08 336093
 
0.7%
0.09 369033
 
0.8%
ValueCountFrequency (%)
172.26 2
 
< 0.1%
147.94 5
 
< 0.1%
145.91 1
 
< 0.1%
141.86 1
 
< 0.1%
137.81 1
 
< 0.1%
135.78 7
< 0.1%
133.75 12
< 0.1%
131.73 16
< 0.1%
129.7 9
< 0.1%
128.69 6
 
< 0.1%

TRT_ID
Real number (ℝ)

Distinct2693
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean146994.67
Minimum20
Maximum4005907
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size355.8 MiB
2023-01-11T11:46:47.416072image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile1199
Q15991
median10320
Q325802
95-th percentile56396
Maximum4005907
Range4005887
Interquartile range (IQR)19811

Descriptive statistics

Standard deviation713230.08
Coefficient of variation (CV)4.8520811
Kurtosis25.267716
Mean146994.67
Median Absolute Deviation (MAD)5071
Skewness5.2209786
Sum6.8549414 × 1012
Variance5.0869715 × 1011
MonotonicityNot monotonic
2023-01-11T11:46:47.497505image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13756 17328
 
< 0.1%
8166 17328
 
< 0.1%
18475 17328
 
< 0.1%
6089 17328
 
< 0.1%
18099 17328
 
< 0.1%
6133 17328
 
< 0.1%
15142 17328
 
< 0.1%
7198 17328
 
< 0.1%
12504 17328
 
< 0.1%
7171 17328
 
< 0.1%
Other values (2683) 46460664
99.6%
ValueCountFrequency (%)
20 17304
< 0.1%
22 17304
< 0.1%
24 17304
< 0.1%
26 17304
< 0.1%
27 17304
< 0.1%
28 17304
< 0.1%
30 17304
< 0.1%
34 17304
< 0.1%
40 17328
< 0.1%
41 17328
< 0.1%
ValueCountFrequency (%)
4005907 17328
< 0.1%
4005667 17304
< 0.1%
4005660 17304
< 0.1%
4005608 17328
< 0.1%
4005591 17304
< 0.1%
4005581 17328
< 0.1%
4005580 17328
< 0.1%
4005572 17304
< 0.1%
4005570 17328
< 0.1%
4005561 17328
< 0.1%

lat,lon
Categorical

Distinct2651
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size355.8 MiB
40.6831,-74.0066
 
64969
37.881,-122.2999
 
58607
35.3074,-119.0981
 
56904
29.6553,-98.6263
 
52532
29.9208,-90.0791
 
51984
Other values (2646)
46348948 

Length

Max length17
Median length16
Mean length16.23421
Min length12

Characters and Unicode

Total characters757065231
Distinct characters13
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row47.4258,-122.2408
2nd row32.8932,-97.4587
3rd row40.8567,-73.1671
4th row34.5199,-105.8701
5th row39.7774,-104.8564

Common Values

ValueCountFrequency (%)
40.6831,-74.0066 64969
 
0.1%
37.881,-122.2999 58607
 
0.1%
35.3074,-119.0981 56904
 
0.1%
29.6553,-98.6263 52532
 
0.1%
29.9208,-90.0791 51984
 
0.1%
30.2285,-81.5908 44063
 
0.1%
38.932,-77.2395 43370
 
0.1%
26.7065,-80.1281 42601
 
0.1%
27.8142,-82.6786 42575
 
0.1%
33.6941,-117.8257 42505
 
0.1%
Other values (2641) 46133834
98.9%

Length

2023-01-11T11:46:47.574051image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
40.6831,-74.0066 64969
 
0.1%
37.881,-122.2999 58607
 
0.1%
35.3074,-119.0981 56904
 
0.1%
29.6553,-98.6263 52532
 
0.1%
29.9208,-90.0791 51984
 
0.1%
30.2285,-81.5908 44063
 
0.1%
38.932,-77.2395 43370
 
0.1%
26.7065,-80.1281 42601
 
0.1%
27.8142,-82.6786 42575
 
0.1%
33.6941,-117.8257 42505
 
0.1%
Other values (2641) 46133834
98.9%

Most occurring characters

ValueCountFrequency (%)
. 93267888
12.3%
1 80942156
10.7%
3 74738161
9.9%
7 62860405
8.3%
2 61045812
8.1%
4 57065366
7.5%
8 56286525
7.4%
9 52167849
6.9%
, 46633944
 
6.2%
- 46633944
 
6.2%
Other values (3) 125423181
16.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 570529455
75.4%
Other Punctuation 139901832
 
18.5%
Dash Punctuation 46633944
 
6.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 80942156
14.2%
3 74738161
13.1%
7 62860405
11.0%
2 61045812
10.7%
4 57065366
10.0%
8 56286525
9.9%
9 52167849
9.1%
6 43772211
7.7%
5 42604468
7.5%
0 39046502
6.8%
Other Punctuation
ValueCountFrequency (%)
. 93267888
66.7%
, 46633944
33.3%
Dash Punctuation
ValueCountFrequency (%)
- 46633944
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 757065231
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 93267888
12.3%
1 80942156
10.7%
3 74738161
9.9%
7 62860405
8.3%
2 61045812
8.1%
4 57065366
7.5%
8 56286525
7.4%
9 52167849
6.9%
, 46633944
 
6.2%
- 46633944
 
6.2%
Other values (3) 125423181
16.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 757065231
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 93267888
12.3%
1 80942156
10.7%
3 74738161
9.9%
7 62860405
8.3%
2 61045812
8.1%
4 57065366
7.5%
8 56286525
7.4%
9 52167849
6.9%
, 46633944
 
6.2%
- 46633944
 
6.2%
Other values (3) 125423181
16.6%

Interactions

2023-01-11T11:43:24.217350image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T11:30:05.088830image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T11:31:47.299888image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T11:33:24.535452image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T11:35:06.426892image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T11:36:52.314892image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T11:38:32.746935image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T11:40:10.134286image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T11:41:47.220984image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T11:43:34.802291image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T11:30:16.207896image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T11:31:57.742011image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T11:33:35.530545image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T11:35:18.275911image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T11:37:03.125175image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T11:38:43.639385image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T11:40:20.905613image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T11:41:57.727729image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T11:43:45.611199image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T11:30:27.825010image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T11:32:08.659789image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T11:33:47.110364image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T11:35:30.244911image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T11:37:14.585041image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T11:38:54.741280image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T11:40:31.716045image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T11:42:08.540543image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T11:43:56.699525image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T11:30:39.908792image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T11:32:20.011297image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T11:33:58.807253image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T11:35:42.246241image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T11:37:26.234392image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T11:39:05.866146image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T11:40:42.936789image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T11:42:19.857389image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T11:44:07.280263image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T11:30:51.317284image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T11:32:30.772772image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T11:34:10.359951image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T11:35:54.155921image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T11:37:37.303057image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T11:39:16.815616image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T11:40:53.778934image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T11:42:30.832587image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T11:44:17.888305image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T11:31:02.701864image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T11:32:41.314204image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T11:34:21.567165image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T11:36:05.897969image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T11:37:48.360672image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T11:39:27.348254image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T11:41:04.530640image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T11:42:41.558151image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T11:44:28.428418image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T11:31:14.136576image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T11:32:51.893149image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T11:34:32.509238image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T11:36:17.584964image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T11:37:59.349352image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T11:39:37.877717image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T11:41:14.932412image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T11:42:52.259983image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T11:44:38.937281image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T11:31:25.458419image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T11:33:02.295555image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T11:34:43.524381image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T11:36:29.231364image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T11:38:10.556294image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T11:39:48.673992image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T11:41:25.639611image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T11:43:02.887578image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T11:44:49.312437image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T11:31:36.602059image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T11:33:13.067844image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T11:34:54.667344image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T11:36:40.888473image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T11:38:21.670246image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T11:39:59.246087image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T11:41:36.349612image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-11T11:43:13.635942image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2023-01-11T11:46:47.639233image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
components.cocomponents.nocomponents.no2components.o3components.so2components.pm2_5components.pm10components.nh3TRT_IDmain.aqi
components.co1.0000.4150.782-0.4240.4600.6040.5400.504-0.0450.333
components.no0.4151.0000.406-0.1800.3590.3570.3390.315-0.0460.243
components.no20.7820.4061.000-0.5580.5790.5630.5460.533-0.0830.334
components.o3-0.424-0.180-0.5581.000-0.000-0.219-0.192-0.2230.0030.375
components.so20.4600.3590.579-0.0001.0000.4430.4160.277-0.0530.182
components.pm2_50.6040.3570.563-0.2190.4431.0000.9390.385-0.0370.023
components.pm100.5400.3390.546-0.1920.4160.9391.0000.379-0.0520.027
components.nh30.5040.3150.533-0.2230.2770.3850.3791.000-0.0410.093
TRT_ID-0.045-0.046-0.0830.003-0.053-0.037-0.052-0.0411.0000.022
main.aqi0.3330.2430.3340.3750.1820.0230.0270.0930.0221.000

Missing values

2023-01-11T11:44:56.945471image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-01-11T11:45:21.902066image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

dtmain.aqicomponents.cocomponents.nocomponents.no2components.o3components.so2components.pm2_5components.pm10components.nh3TRT_IDlat,lon
02021-01-011257.020.0111.6548.642.802.674.790.25683847.4258,-122.2408
12021-01-011230.310.005.6660.800.650.680.860.123561832.8932,-97.4587
22021-01-011297.070.0115.2549.352.471.542.610.85985240.8567,-73.1671
32021-01-011183.580.072.2175.820.142.885.231.171780134.5199,-105.8701
42021-01-012403.883.2167.1713.771.6416.7621.831.823590539.7774,-104.8564
52021-01-011323.770.0221.5957.222.565.977.413.613415545.0462,-93.3921
62021-01-014540.733.1654.1580.1110.9744.6751.600.40577332.5869,-117.0917
72021-01-012210.290.202.2794.410.523.113.980.353117734.6133,-118.1406
82021-01-011257.020.006.8665.801.030.591.200.555860241.0784,-74.1667
92021-01-012360.492.6834.9680.119.3013.2616.721.87572333.7245,-117.7962
dtmain.aqicomponents.cocomponents.nocomponents.no2components.o3components.so2components.pm2_5components.pm10components.nh3TRT_IDlat,lon
466339342022-12-31 23:00:001260.350.2212.5120.560.783.944.990.352639130.5102,-87.2174
466339352022-12-31 23:00:001360.494.5827.084.651.685.276.780.302089937.4123,-122.1479
466339362022-12-31 23:00:001240.330.2914.5745.064.952.253.380.51997134.0851,-118.1406
466339372022-12-31 23:00:001196.930.061.7853.640.291.272.030.042507634.1533,-118.7617
466339382022-12-31 23:00:001453.954.7539.764.161.488.3911.310.911355742.3967,-83.2776
466339392022-12-31 23:00:001310.421.2927.7616.452.623.444.560.493459537.4255,-121.9754
466339402022-12-31 23:00:002333.791.5822.9655.076.0816.0420.210.992489430.0194,-95.2863
466339412022-12-31 23:00:001267.030.6615.7732.541.622.853.860.3585647.6294,-122.1478
466339422022-12-31 23:00:002213.620.083.9063.660.260.600.810.1667737.9634,-122.516
466339432022-12-31 23:00:001213.620.082.7853.640.312.414.530.144530832.6972,-117.134